With an increasing demand for security and safety, video-based surveillance systems are being increasingly used in urban locations. Vast amounts of video footage are collected and analyzed for traffic violations, accidents, crime, terrorism, vandalism, and other suspicious activities. Since manual analysis of such large volumes of data is prohibitively costly, there is a desire to develop effective algorithms that can aid in the automatic or semi-automatic interpretation and analysis of video data for surveillance and law enforcement. An active area of research within this domain is video anomaly detection, which refers to the problem of finding patterns in data that do not conform to expected behavior, and that may warrant special attention or action. The focus of this invention is in the detection of anomalies in the transportation domain.
Examples include traffic violations, unsafe driver and pedestrian behavior, accidents, etc. FIGS. 1(a)-1(d) depict some example of transportation related anomalies. For example, in FIG. 1(a), image 102 depicts a lobby with individuals walking with respect to an anomalous area 103. FIG. 1(b) depicts an image 104 of a street scene with an anomalous area 105. FIG. 1(c) depicts an image 106 showing a street scene including a bicyclist and a two automobiles. FIG. 1(d) depicts an image 108 of a street scene including an automobile passing through an intersection.
Video-based anomaly detection (AD) has recently received much recent attention. One class of techniques relies upon object tracking to detect nominal object trajectories and deviations thereof. This approach is appealing for traffic-related anomalies since there are many state-of-the-art tracking techniques that can be leveraged. A common approach is to derive nominal vehicle paths and look for deviations thereof in live traffic video data. During the test or evaluation phase, a vehicle is tracked and its path compared against the nominal classes. A statistically significant deviation from all classes indicates an anomalous path.
Primary challenges in anomaly detection include: i) successful detection of abnormal patterns in realistic scenarios involving multiple object trajectories in the presence of occlusions, clutter, and other background noise; ii) development of algorithms that are computationally simple enough to detect anomalies in quasi-real-time; and iii) the lack of sufficient and standardized data sets, particularly those capturing anomalous events which are rare by definition.
A new breed of techniques based on sparse reconstruction has been successfully applied towards anomaly detection. One potential limitation of the approach is that the effectiveness of the sparsity model largely relies on the structure of training data. If the event classes are not sufficiently linearly separable, the sparse reconstruction may not result in accurate anomaly detection.